Accent Bias in Speech Recognition

ASR converts speech into text and can fail through substitutions, deletions or insertions. Word error rate (WER) combines these errors relative to a reference transcript, but one overall WER can conceal unequal performance across speakers. A 2020 PNAS study tested five commercial systems from Amazon, Apple, Google, IBM and Microsoft on structured interviews with 42 White speakers and 73 Black speakers. The mean WER was 0.35 for Black speakers and 0.19 for White speakers in that sample. The authors measured race-associated differences in a U.S. setting; this should not be recast as an estimate for every Black speaker or an accent-only causal effect.

Other research isolates accent more directly. A 2023 study evaluated English ASR across regional and non-native accents and also examined Dutch and Mandarin systems; its results found substantial variation by accent and architecture, and showed that accent robustness is a system- and language-specific question. Accent, dialect, race, language proficiency, recording quality and microphone conditions overlap but are not interchangeable. A speaker may use a regional accent without being non-native, or speak a dialect with systematic grammatical features.

A higher error rate matters when ASR mediates access to captions, phone menus, clinical documentation or employment interviews. Errors can change meaning, require repeated effort or create incorrect records. The evidence does not justify a universal ranking of accents or a claim that one system is always worst. It supports testing the actual product and speakers in its intended context, providing correction routes and avoiding high-stakes decisions based solely on unverified transcripts.